Heart DT: Monitoring and Preventing Cardiac Pathologies Using AI and IoT Sensors

Author:

Avanzato Roberta1ORCID,Beritelli Francesco1,Lombardo Alfio1,Ricci Carmelo12

Affiliation:

1. Department of Electrical, Electronic and Computer Engineering, University of Catania, Viale Andrea Doria, 95125 Catania, Italy

2. CNIT—Research Unit of the Catania, National Inter-University Consortium for Telecommunications, 43124 Parma, Italy

Abstract

Today’s healthcare facilities require new digital tools to cope with the rapidly increasing demand for technology that can support healthcare operators. The advancement of technology is leading to the pervasive use of IoT devices in daily life, capable of acquiring biomedical and biometric parameters, and providing an opportunity to activate new tools for the medical community. Digital twins (DTs) are a form of technology that are gaining more prominence in these scenarios. Many scientific research papers in the literature are combining artificial intelligence (AI) with DTs. In this work, we propose a case study including a proof of concept based on microservices, the heart DT, for the evaluation of electrocardiogram (ECG) signals by means of an artificial intelligence component. In addition, a higher-level platform is presented and described for the complete management and monitoring of cardiac pathologies. The overall goal is to provide a system that can facilitate the patient–doctor relationship, improve medical treatment times, and reduce costs.

Funder

National Operational Plan (PON) Project 4FRAILTY

Publisher

MDPI AG

Subject

Computer Networks and Communications

Reference41 articles.

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5. Avanzato, R., and Beritelli, F. (2022, January 24–26). Heart disease recognition based on extended ECG sequence database and deep learning techniques. Proceedings of the 2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS), Bali, Indonesia.

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